电讯技术2026，Vol.66Issue(2)：173-182,10.DOI:10.20079/j.issn.1001-893x.241009002
空天地协同网络的边缘计算与资源分配
Edge Computing Offloading and Resource Allocation for Space-Air-Ground Collaborative Network
摘要
Abstract
Low Earth orbit(LEO)satellite and high altitude platform(HAP)have become the key technologies to achieve all-domain,full-time,and full-coverage communication.In order to better provide efficient and stable services for ground users,aiming at the HAP-assisted LEO satellite edge computing,a three-layer network architecture composed of terminals,HAPs,and LEO satellites is proposed,and the tasks can be processed on the three platforms,and the satellites can also cooperate to achieve on-board load balancing.Considering the problems of time delay,resource constraints,the high complexity of the modeling problem and the rapid fading of satellite-to-ground channels,by jointly optimizing offload decisions,bandwidth and computing resource allocation strategies,a task offloading and resource allocation algorithm based on deep deterministic policy gradient is proposed,which models the problem as a Markov decision process,and preprocesses the environmental state parameters by state normalization algorithm.Compared with three strategy algorithms including deep Q network,full offloading and no inter-satellite link,the proposed algorithm shows excellent performance in terms of delay and energy consumption.关键词
低轨卫星/高空平台/移动边缘计算/卸载决策/资源分配/深度强化学习Key words
LEO satellite/high altitude platform/mobile edge computing/offload strategy/resource allocation/deep reinforcement learning分类
信息技术与安全科学引用本文复制引用
杨黎明,周玉前,金宇峰,赵鸿俊..空天地协同网络的边缘计算与资源分配[J].电讯技术,2026,66(2):173-182,10.基金项目
重庆市自然科学基金创新发展联合基金(中国星网)(CSTB2023NSCQ-LZX0114) （中国星网）
